Using neural networks to create a reliable phase quality map for phase unwrapping.

Two-dimensional phase unwrapping is a crucial step in interferometric signal processing. A phase quality map can help the unwrapping algorithm deal with low-quality and fast-changing regions. However, because existing algorithms cannot calculate a quality map representing the gradient quality directly, it is usually necessary to approximate the gradient quality with phase quality to assist the network-based phase unwrapping algorithm. Furthermore, they cannot withstand intense noise in low-quality regions, resulting in many errors in path-based algorithms. To address the aforementioned issues, this paper analyzes the essence of a quality map and proposes a quality map generation method based on a convolutional neural network. The generated quality maps are a pair, each indicating the quality of horizontal and vertical gradients. Experiments show that the quality map generated by this method can help path-based and network-based algorithms perform better.

Hierarchical quality-guided phase unwrapping algorithm.

A hierarchical quality-guided phase unwrapping algorithm in a shared memory environment is proposed. First, the wrapped phase is divided into regular blocks, and local wrap counts of every block are obtained by a quality-guided strategy. Then, the gradient of the block wrap counts of adjacent blocks and the quality of every block are defined by the boundary local wrap counts of every block. Each block's data can be regarded as an abstract phase point, and the quality-guided strategy can be used again to solve the block wrap counts of each block. Finally, the absolute wrap counts of each phase point are obtained by adding local wrap counts and corresponding block wrap counts, and then the final unwrapped phase is obtained. The performance of the proposed algorithm is verified through an unwrapping experiment performed on simulated data and the real interferometric synthetic aperture sonar wrapped phase in a shared memory environment. The results show that the proposed method greatly improves phase unwrapping efficiency while maintaining the correctness of unwrapped results.

A Correction Approach for the Inclined Array of Hydrophones in Synthetic Aperture Sonar.

A correction approach for the inclined array of hydrophones is proposed to prevent decline of the image quality in SAS. In this approach, the 2-way exact acoustic propagation path of the inclined array is transformed into the sum of a single root term and an offset term, where the single root term is the 2-way ideal propagation path and the offset term contains all errors cause by the inclined array. The correction for the offset term is separated into two steps: phase correction and delay correction. The phase correction is performed on the echo signal of each receiving hydrophone in the 2-D time domain by a phase multiplication and the delay correction is performed on the echo signal of each receiving hydrophone in the range frequency domain by a phase multiplication with a linear function of range frequency at the reference range. Finally, the effectiveness of the proposed approach is examined by the simulation experiments.

Phase quality map based on local multi-unwrapped results for two-dimensional phase unwrapping.

The efficiency of a phase unwrapping algorithm and the reliability of the corresponding unwrapped result are two key problems in reconstructing the digital elevation model of a scene from its interferometric synthetic aperture radar (InSAR) or interferometric synthetic aperture sonar (InSAS) data. In this paper, a new phase quality map is designed and implemented in a graphic processing unit (GPU) environment, which greatly accelerates the unwrapping process of the quality-guided algorithm and enhances the correctness of the unwrapped result. In a local wrapped phase window, the center point is selected as the reference point, and then two unwrapped results are computed by integrating in two different simple ways. After the two local unwrapped results are computed, the total difference of the two unwrapped results is regarded as the phase quality value of the center point. In order to accelerate the computing process of the new proposed quality map, we have implemented it in a GPU environment. The wrapped phase data are first uploaded to the memory of a device, and then the kernel function is called in the device to compute the phase quality in parallel by blocks of threads. Unwrapping tests performed on the simulated and real InSAS data confirm the accuracy and efficiency of the proposed method.

Spectral Normalized CycleGAN with Application in Semisupervised Semantic Segmentation of Sonar Images.

The effectiveness of CycleGAN is demonstrated to outperform recent approaches for semisupervised semantic segmentation on public segmentation benchmarks. In contrast to analog images, however, the acoustic images are unbalanced and often exhibit speckle noise. As a consequence, CycleGAN is prone to mode-collapse and cannot retain target details when applied directly to the sonar image dataset. To address this problem, a spectral normalized CycleGAN network is presented, which applies spectral normalization to both generators and discriminators to stabilize the training of GANs. Without using a pretrained model, the experimental results demonstrate that our simple yet effective method helps to achieve reasonably accurate sonar targets segmentation results.

Succession rate of microbial community causes flavor difference in strong-aroma Baijiu making process.

Solid-state fermentation is a dynamic process involved with complex microbiome. Microbial structure and succession significantly affect the yield and quality of fermentation productions. Although the importance of microbial structure was extensively studied, the significance of microbial succession rate remains unclear in solid-state fermentation. To address this gap, we designed an in situ experiment in a typical distillery to characterize the effects of microbial succession rate. In this study, we found the process of strong-aroma Baijiu making could be divided into two stages according to fermentation parameters (starch, moisture, acidity, reducing sugar, alcohol, temperature). The early stage showed significantly (p < 0.05) higher microbial diversity than that of the later stage according to Shannon index. Compared with single cereal fermentation, mixed cereals fermentation showed slower microbial succession rate of stage shift. We found that Lactobacillus could reflect microbial succession rate of stage shift in strong-aroma Baijiu fermentation. Meanwhile, we found fermentation parameters could affect microbial succession rate of stage shift. Microbial diversity was significantly (p < 0.05) correlated with fermentation parameters. Moreover, molecular ecological network analysis (MENA) showed that succession rate of microbial community could affect microbial interactions. In addition, fermentation of mix cereals (sorghum, wheat, corn, rice and glutinous rice) increased the enrichment of Clostridiales from pit mud according to results of source tracking. Collectively, succession rate of microbial community could be an important trait to explain differences of microbial diversity and flavor profile from the perspective of microbial decline and enrichment. Our study highlighted the importance of microbial succession rate during strong-aroma Baijiu making process and provided a dynamic perspective to observe solid-state fermentation.